Attitudes of Veterinary Faculty to the Assessment of Clinical Reasoning Using Extended Matching Questions
Bibliographic record
Abstract
For assessment purposes, clinical expertise is often divided into three broad components: scientific and clinical knowledge, clinical reasoning, and practical/technical skills. This structure can be used to define the tools used for assessment of clinical students. Knowledge can be assessed through a variety of written formats and skills through various practical assessments, including the objective structured clinical examination (OSCE), but the assessment of clinical reasoning has proved to be far more challenging. A companion paper (Tomlin JL, Pead MJ, May SA. Veterinary students' attitudes toward the assessment of clinical reasoning using extended matching questions. J Vet Med Educ 35:612-621, 2008) reports on the identification and implementation of a valid and reliable method to assess clinical reasoning using clinical-scenario-based extended matching questions (EMQs) in the final examinations at the Royal Veterinary College and looks at students' response to the new examination format. Although EMQs were generally well accepted, many students were concerned about the implied encouragement of pattern recognition, a non-analytical form of clinical reasoning that results from recognition of familiar clinical situations. This paper addresses the attitudes of the teaching faculty to the EMQ format. The students' concerns about promotion of pattern recognition, was also explored in more depth. Overall, faculty perceived EMQs as an appropriate way to test clinical reasoning and as relevant to the experience that students would have gained during their clinical rotations. However, faculty felt that EMQs were difficult to write and that poorly written questions tended to promote pattern recognition. Almost half reiterated the students' concerns that pattern recognition may be an inappropriate reasoning strategy for undergraduates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".